Iceberg#

Apache Iceberg is an open table format for huge analytic datasets. Iceberg adds tables to compute engines including Spark, Trino, PrestoDB, Flink, Hive and Impala using a high-performance table format that works just like a SQL table.

Tip

This article assumes that you have mastered the basic knowledge and operation of Iceberg. For the knowledge about Iceberg not mentioned in this article, you can obtain it from its Official Documentation.

By using kyuubi, we can run SQL queries towards Iceberg which is more convenient, easy to understand, and easy to expand than directly using flink to manipulate Iceberg.

Iceberg Integration#

To enable the integration of kyuubi flink sql engine and Iceberg through Catalog APIs, you need to:

Dependencies#

The classpath of kyuubi flink sql engine with Iceberg supported consists of

  1. kyuubi-flink-sql-engine-1.7.0-SNAPSHOT_2.12.jar, the engine jar deployed with Kyuubi distributions

  2. a copy of flink distribution

  3. iceberg-flink-runtime-<flink.version>-<iceberg.version>.jar (example: iceberg-flink-runtime-1.14-0.14.0.jar), which can be found in the Maven Central

In order to make the Iceberg packages visible for the runtime classpath of engines, we can use one of these methods:

  1. Put the Iceberg packages into $FLINK_HOME/lib directly

  2. Set pipeline.jars=/path/to/iceberg-flink-runtime

Warning

Please mind the compatibility of different Iceberg and Flink versions, which can be confirmed on the page of Iceberg multi engine support.

Iceberg Operations#

Taking CREATE CATALOG as a example,

CREATE CATALOG hive_catalog WITH (
  'type'='iceberg',
  'catalog-type'='hive',
  'uri'='thrift://localhost:9083',
  'warehouse'='hdfs://nn:8020/warehouse/path'
);
USE CATALOG hive_catalog;

Taking CREATE DATABASE as a example,

CREATE DATABASE iceberg_db;
USE iceberg_db;

Taking CREATE TABLE as a example,

CREATE TABLE `hive_catalog`.`default`.`sample` (
  id BIGINT COMMENT 'unique id',
  data STRING
);

Taking Batch Read as a example,

SET execution.runtime-mode = batch;
SELECT * FROM sample;

Taking Streaming Read as a example,

SET execution.runtime-mode = streaming;
SELECT * FROM sample /*+ OPTIONS('streaming'='true', 'monitor-interval'='1s')*/ ;

Taking INSERT INTO as a example,

INSERT INTO `hive_catalog`.`default`.`sample` VALUES (1, 'a');
INSERT INTO `hive_catalog`.`default`.`sample` SELECT id, data from other_kafka_table;

Taking INSERT OVERWRITE as a example, Flink streaming job does not support INSERT OVERWRITE.

INSERT OVERWRITE `hive_catalog`.`default`.`sample` VALUES (1, 'a');
INSERT OVERWRITE `hive_catalog`.`default`.`sample` PARTITION(data='a') SELECT 6;